_base_ = [
    "../_base_/models/mask_rcnn_r50_fpn.py",
    "../_base_/datasets/lvis_v0.5_instance.py",
    "../_base_/schedules/schedule_2x.py",
    "../_base_/default_runtime.py",
]
model = dict(
    roi_head=dict(bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)),
    test_cfg=dict(
        rcnn=dict(
            score_thr=0.0001,
            # LVIS allows up to 300
            max_per_img=300,
        )
    ),
)
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True, with_mask=True),
    dict(
        type="Resize",
        img_scale=[
            (1333, 640),
            (1333, 672),
            (1333, 704),
            (1333, 736),
            (1333, 768),
            (1333, 800),
        ],
        multiscale_mode="value",
        keep_ratio=True,
    ),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels", "gt_masks"]),
]
data = dict(train=dict(dataset=dict(pipeline=train_pipeline)))
